2022 18th International Conference on Network and Service Management (CNSM)最新文献

ΔQ Generative Models: Modeling Time-Variation in Network Quality ΔQ生成模型:网络质量时变建模

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9965128

Bjørn Ivar Teigen, N. Davies, P. Thompson, K. Ellefsen, T. Skeie, J. Tørresen

引用次数: 0

FED-UP: Federated Deep Reinforcement Learning-based UAV Path Planning against Hostile Defense System 基于联邦深度强化学习的无人机路径规划对抗敌方防御系统

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964907

Alvi Ataur Khalil, M. Rahman

{"title":"FED-UP: Federated Deep Reinforcement Learning-based UAV Path Planning against Hostile Defense System","authors":"Alvi Ataur Khalil, M. Rahman","doi":"10.23919/CNSM55787.2022.9964907","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964907","url":null,"abstract":"In military operations, unmanned aerial vehicles (UAVs) have been heavily utilized in recent years. However, due to the antenna installment regulation, UAVs cannot be controlled by human operators in a restricted area. Hence, artificial intelligence (AI)-driven UAVs are the practical solution to this out-of-coverage problem. With the increased use of autonomous UAVs in military applications, defense systems are deployed to target and shoot down the enemy UAVs in operation. Thus, UAVs are needed to be trained, not only to achieve goals but also to avoid static and dynamic hostile defense systems. In this work, we propose FED-UP, a federated deep reinforcement learning (DRL)-based UAV path planning framework, that enables UAVs to carry out missions in a hostile environment with a dynamic defense system. The federated learning (FL) based training accelerates the reinforcement learning process and improves model performance. We additionally introduce significant reply memory buffer (SRMB) to quicken the training process more, by selecting the crucial experiences during the training period. The experimental results validate the efficiency of the proposed model in controlling UAVs in dynamic, hostile environments.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Flow-level Tail Latency Estimation and Verification based on Extreme Value Theory 基于极值理论的流级尾延迟估计与验证

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964525

Max Helm, Florian Wiedner, G. Carle

引用次数: 1

Learning to Caching Under the Partial-feedback Regime 在部分反馈机制下学习缓存

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964551

Qingsong Liu, Yaoyu Zhang

{"title":"Learning to Caching Under the Partial-feedback Regime","authors":"Qingsong Liu, Yaoyu Zhang","doi":"10.23919/CNSM55787.2022.9964551","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964551","url":null,"abstract":"We consider the caching problem in an online learning perspective, i.e., no model assumptions and prior knowledge for the file request sequence. Our goal is to design an efficient on-line caching policy with minimal regret, i.e, minimizing the total number of cache-miss with respect to the best static configuration in hindsight. Previous studies such as Follow-The-Perturbed-Leader (FTPL) caching policy, have provided some near-optimal results, but their theoretical performance guarantees only valid for the regime wherein all arrival requests could be seen by the cache, which is not the case in some practical scenarios like caching at cellular base station, content dissemination via DNS, etc. Hence our work study the partial-feedback regime wherein only requests for currently cached files are seen by the cache, which is more challenging and has not been studied before in the online learning perspective. We propose an online caching policy combining the FTPL with a novel popularity estimation procedure called Geometric Resampling (GR), and show that it yields the first sublinear regret guarantee in this regime. We also conduct numerical experiments to validate the theoretical guarantees of our caching policy.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124586208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

PerfTrace: A New Multi-metric Network Performance Monitoring Tool PerfTrace:一种新的多度量网络性能监控工具

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964543

Yaozhong Liu, Long Pan, Chenglong Li, Lin He, Yirui Luo, Guanglei Song, Jiahai Yang, Zhiliang Wang

引用次数: 0

CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming 用于HTTP自适应流的设备感知比特率阶梯结构

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964669

Minh Nguyen, Babak Taraghi, A. Bentaleb, Roger Zimmermann, C. Timmerer

{"title":"CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming","authors":"Minh Nguyen, Babak Taraghi, A. Bentaleb, Roger Zimmermann, C. Timmerer","doi":"10.23919/CNSM55787.2022.9964669","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964669","url":null,"abstract":"In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages the Common Media Client Data (CMCD) standard to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123993350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Accelerating Causal Inference Based RCA Using Prior Knowledge From Functional Connectivity Inference 利用功能连接推理的先验知识加速基于因果推理的RCA

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964900

Giles Winchester, G. Parisis, Robert Harper, L. Berthouze

引用次数: 1

Howdah: Load Profiling via In-Band Flow Classification and P4 Howdah:基于带内流分类和P4的负载分析

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964510

Antonino Angi, Alessio Sacco, Flavio Esposito, G. Marchetto, A. Clemm

{"title":"Howdah: Load Profiling via In-Band Flow Classification and P4","authors":"Antonino Angi, Alessio Sacco, Flavio Esposito, G. Marchetto, A. Clemm","doi":"10.23919/CNSM55787.2022.9964510","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964510","url":null,"abstract":"The challenges of managing datacenter traffic increase with the complexity and variety of new Internet and Web applications. Efficient network management systems are often required to thwart delays and minimize failures. In this regard, it appears helpful to identify in advance the different classes of flows that (co)exist in the network, characterizing them into different types according to the different latency/bandwidth requirements. In this paper, we propose Howdah, a traffic identification and profiling mechanism that uses Machine Learning and a congestion-aware forwarding strategy to offer adaptation to different traffic classes with the support of programmable data-planes. With Howdah, sender and gateway elements inject in-band traffic information obtained using supervised learning. When a switch or a router receives a packet, it exploits such host-based traffic classification to adapt to a desirable traffic profile, for example, balancing the load. We compare our solutions against recent traffic engineering solutions and show the efficacy of cooperation between host traffic classification and P4-based switch forwarding policies, reducing packet transmission time in datacenter scenarios.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129116595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing Traditional and GAN-based Approaches for the Synthesis of Wide Area Network Topologies 广域网拓扑综合的传统方法与基于gan的方法的比较

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964866

Katharina Dietz, Michael Seufert, T. Hossfeld

{"title":"Comparing Traditional and GAN-based Approaches for the Synthesis of Wide Area Network Topologies","authors":"Katharina Dietz, Michael Seufert, T. Hossfeld","doi":"10.23919/CNSM55787.2022.9964866","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964866","url":null,"abstract":"Wide Area Network (WAN) research benefits from the availability of realistic network topologies, e.g., as input to simulations, emulators, or testbeds. With the rise of Machine Learning (ML) and particularly Deep Learning (DL) methods, this demand for topologies, which can be used as training data, is greater than ever. However, public datasets are limited, thus, it is promising to generate synthetic graphs with realistic properties based on real topologies for the augmentation of existing data sets. As the generation of synthetic graphs has been in the focus of researchers of various applications fields since several decades, we have a variety of traditional model-dependent and model-independent graph generators at hand, as well as DL-based approaches, such as Generative Adversarial Networks (GANs). In this work, we adapt and evaluate these existing generators for the WAN use case, i.e., for generating synthetic WANs with realistic geographical distances between nodes. Moreover, we investigate a hierarchical graph synthesis approach, which divides the synthesis into local clusters. Finally, we compare the similarity of synthetic and real WAN topologies and discuss the suitability of the generators for data augmentation in the WAN use case.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Online Framework for Adapting Security Policies in Dynamic IT Environments 动态IT环境中适应安全策略的在线框架

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964838

K. Hammar, R. Stadler

引用次数: 4